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Improving Marketing ROI for an E-Commerce Brand Using BI-Powered Ad Spend Analytics

Improving Marketing ROI for an E-Commerce Brand Using BI-Powered Ad Spend Analytics

A fast-growing direct-to-consumer (D2C) e-commerce company operating in the fashion and accessories segment struggled to understand the true return on investment (ROI) from its multi-channel marketing efforts. With significant monthly ad spend across Meta, Google Ads, and influencer campaigns, the brand lacked unified visibility into which channels were driving profitable customer conversions and long-term value. To address this, a robust Business Intelligence (BI) solution was implemented to centralize campaign, conversion, and customer lifetime value (LTV) data. By integrating marketing analytics, purchase history, and CRM data, the company was able to track return on ad spend (ROAS) in real time, segment customer cohorts, and optimize budget allocation toward the highest-performing sources

Image representing case study

35%

improvement in ROAS within the first 3 months

2x

increase in high-LTV customer acquisition from email campaigns

28%

decrease in average CAC

About  

Problem Statement

  • Detached information sources made it troublesome to trait deals to particular campaigns or stages
  • The team lacked a unified dashboard to compare spend, conversion rates, and customer LTV
  • Marketing budgets were often allocated reactively rather than based on performance
  • High ad spends were generating traffic but not always driving profitable conversions
  • Influencer and email marketing efforts lacked measurable impact tracking

Industry: E-commerce (Fashion & Accessories)

Services: Business Intelligence Implementation, ROI Tracking, Campaign Attribution Modeling

Region: North America (Shipping globally)

Our Tech Stack

Tech stack we used

Solution Approach

  • Solidified advertisement execution information from Meta, Google Advertisements, and influencer spreadsheets into a centralized information stockroom (BigQuery)
  • Integrated Shopify sales and CRM data to attribute conversions to specific campaigns
  • Developed ROAS dashboards segmented by platform, campaign, ad creative, and landing page
  • Built LTV prediction models in Python to evaluate long-term impact of each acquisition source
  • Identified underperforming campaigns and reallocated budget to high-ROAS ad sets
  • Enabled the marketing team with real-time dashboards to guide daily budget adjustments
  • Deployed weekly automated reports for C-level decision-makers and campaign owners

Benefits

  • 35% increment in mixed ROAS over all paid channels
  • 28% drop in customer acquisition costs over 6 months
  • Improved marketing efficiency by identifying low-performing ad creatives and campaigns
  • Higher revenue per user by focusing on channels with better LTV
  • Real-time bits of knowledge enabled showcasing groups to form speedier, data-driven choices

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